Use of data in drug manufacturing has increased in recent years, says Phil Braun, director of client solutions at NECI. He adds that the emergence of new product types, the focus on rare diseases, and technology advances are driving the change.
“The influx of new therapeutic modalities and small market/rare disease, aka non-blockbuster, drives a relook at the business as a whole,” he notes. “The shift to smaller manufacturing volumes, flexible facilities, innovation in compliance approaches, rapid release paradigms are causing upstart organizations to consider digital out of the gate, aka ‘greenfield digital.’ This is matched by advances in technology (cloud, big data lessons learned for enterprise data context) that enable digital.”
He cites COVID-19 vaccine developer, Moderna, as an example of a firm that has adopted a digital strategy, explaining the approach helped speed up development activities.
“There are ‘digital unicorns’ like Moderna which since 2016 had had the goal of being the first ‘all digital biotech,’” continues Braun. What began early in their R&D, digital innovation has permeated the culture and carried through to native digital clinical manufacturing.
“As they near the potential to commercialize for their COVID-19 vaccine, the commitment to digital has fueled their ability to start-up their new vaccine product candidate,” says Braun. “The timeframe from gene sequence to process development to clinical manufacturing is completely different than traditional biopharma. The role of digital is compelling in this case.”
For companies looking to digitize manufacturing operations piecemeal over a longer period, the process is likely to be more challenging and complex.
“Traditional biopharma has grown up like a ‘digital Frankenstein’—siloed decision making along traditional lines separating operations like R&D, clinical trials, commercialization, and quality control has created islands of automation, data, and analytics,” says Braun.
For such firms, Braun explains, digitization requires consistent data contextualization and management practices across the whole enterprise to enable process analytics, supply chain visibility, and predictive quality assessment.
“This is a mammoth challenge and investment, given the starting points in the ‘islands’ across the enterprise,” he points out. And the connectivity that does result to tie the data together creates definite challenges in cybersecurity and performance.”
Amgen is a good example of a firm that successfully pooled data to create a “data lake.” Although the process was costly, according to Braun, “we understand that the investment was very, very significant.”
Another advantage of adopting a digital manufacturing strategy early is that it makes it easier to plan for commercial-scale production.
Traditional process development tends to focus including technologies and sensors required to measure that overall goals have been achieved according to Braun, who says systems that would make commercial production more efficient are often overlooked.
“Many bioprocess decisions are driven early in the PD lab or in pilot operation,” according to Braun. “The goal there is process endpoints supported by data but not necessarily scalability. Therefore, the automation and digital decisions for future manufacturing, even at the basic connect and collect level are often underserved as the initial decision points don’t include digital endpoints, but rather process endpoints.”
Fortunately, this potential shortcoming has been recognized by the bioprocessing technology sector, Braun says.
“We see equipment OEMs understanding this challenge more and more and looking to automation digital experts as part of their teams to better capitalize on the outcomes promised by holistic digital design,” points out Braun.
“One of our big roles is to be the glue amongst the disparate systems that enter a clinical commercial facility such the objectives of multiple manufacturing stakeholders are met regardless of the unit op starting points.”